library(climatic4economist)5: Compute Standardize Evotranspiration Index based on spatial point
1 Introduction
In this tutorial, we extract the CHIRPS precipitation observations in Suriname, based on the centroids of the Principal Sampling Units (PSU) of the Suriname Survey of Living Conditions for the years 2016/17 and 2022. Based on the extracted precipitation, the tutorial shows how to compute the Standardized Precipitation Index (SPI) and merge it with the survey based on the date of interviews.
2 Code
2.1 Set up
We start by setting up the stage for our analysis.
First, we load the necessary packages. We load only climatic4economist package that contains several functions meant to extract and merge spatial variables with surveys. During the tutorial we will use other packages but instead of loading all the package at the begging we will call specific function each time.
In the setup, we also want to create the paths to the various data sources and load the necessary functions for extraction. Note .. means one step back to the folder directory, i.e. one folder back.
Note that how to set up the paths depends on your folder organization but there are overall two approaches:
- you can use the
R project, by opening the project directly you don’t need to set up the path to the project. Automatically the project figures out on its own where it is located in the computer and set that path as working folder. - you can manually set the working folder with the function
setwd().
# path to data folder
path_to_data <- file.path("..",
"..", "data")
# survey
path_to_survey <- file.path(path_to_data, "survey", "AFR")
# administrative division
path_to_adm_div <- file.path(path_to_data, "adm_div", "geoBoundaries")
# weather variables
path_to_pre_monthly <- file.path(path_to_data, "weather", "ERA5_Land", "monthly_pre",
"era5_monthly_avg_total_pre_50_25.nc")
path_to_evo_monthly <- file.path(path_to_data, "weather", "ERA5_Land", "monthly_evo",
"era5_monthly_total_evo_50_25.nc")
# to result folder
path_to_result <- file.path(path_to_data, "result")- 1
- concatenate the string to make a path
- 2
-
..means one folder back
2.2 Read the data
2.2.1 Survey
We start by reading the surveys data. The surveys must have an id that uniquely identifies the household and the coordinates of their interviews.
The next steps are a bit convoluted. Lets split one by one.
list.files(path_to_survey, full.names = TRUE). The surveys are stored in two files. We uselist.files()to list the files.lapply(haven::read_dta). We use thelapply()to apply to each of them the functionhaven::read_dta(). This last function actually readdtafiles into R. The result are two separate block of data, each for each file. They are two separated item within a list.- At this point we can bind the two separate data into a single one with the function
dplyr::bind_rows(). We could have done it before but like this we ensured the two data sets have the same columns.
srvy <- list.files(path_to_survey, pattern = "dta$", full.names = TRUE) |>
lapply(haven::read_dta) |>
dplyr::bind_rows() |>
dplyr::filter(country != "Uganda") |>
# dplyr::select(dplyr::any_of(c("country", "year", "hhid", "lat", "lon", "interview_date"))) |>
dplyr::group_by(country) |>
dplyr::mutate(interview_date = clock::date_parse(interview_date, format = "%d/%m/%Y"),
survey_year = clock::get_year(median(interview_date, na.rm = TRUE)),
survey = paste0(country, substr(survey_year, 3, 4)),
survey = gsub(" ", "", survey),
.before = hhid)
surveys <- srvy$survey |> unique() |> sort()
surveys[1] "BurkinaFaso22" "Ethiopia19" "Malawi19" "Mali22"
[5] "Nigeria18" "Tanzania21" "Togo22" We can keep all the surveys together but the size can be a challenge. Therefore, we split them with the function dplyr::group_split(). Now, each survey is a separate block, but they are all stored in the same list. We also distinguish the surveys that have spatial coordinates from the others.
no_coord_srvy <- c("Nigeria18", "Tanzania21")
srvy_adm_cntry <- srvy |>
dplyr::filter(survey %in% no_coord_srvy) |>
dplyr::select(-c(ea_id, TA_code, ea_code,
zone_code, city_code, subcity_code)) |>
dplyr::mutate(dplyr::across(.cols = dplyr::where(is.character),
.fns = ~ifelse(.x == "", NA_character_, .x)),
dplyr::across(.cols = dplyr::where(labelled::is.labelled),
.fns = labelled::to_character)) |>
dplyr::group_by(survey) |>
dplyr::group_split() |>
purrr::map(janitor::remove_empty,
which = "cols") |>
setNames(no_coord_srvy) |>
purrr::map(\(x) x |>
dplyr::mutate(dplyr::across(.cols = dplyr::where(is.character),
.fns = ~ ifelse(.x == "" | grepl("CONFI", .x),
NA_character_, .x)) )) |>
purrr::map(janitor::remove_empty, which = "cols")
srvy_adm_cntry$Nigeria18
# A tibble: 40,183 × 17
survey_year hhid country admin1 admin2 admin3 lat lon interview_date
<int> <chr> <chr> <chr> <chr> <chr> <dbl> <dbl> <date>
1 2018 10001 Nigeria South East Abia Abia NA NA 2018-08-20
2 2018 10002 Nigeria South East Abia Abia NA NA 2018-08-21
3 2018 10003 Nigeria South East Abia Abia NA NA 2018-08-20
4 2018 10004 Nigeria South East Abia Abia NA NA 2018-08-21
5 2018 10005 Nigeria South East Abia Abia NA NA 2018-08-21
6 2018 10008 Nigeria South East Abia Abia NA NA 2018-08-21
7 2018 10009 Nigeria South East Abia Abia NA NA 2018-08-22
8 2018 10010 Nigeria South East Abia Abia NA NA 2018-08-20
9 2018 10031 Nigeria South East Abia Abia NA NA 2018-08-07
10 2018 10032 Nigeria South East Abia Abia NA NA 2018-08-08
# ℹ 40,173 more rows
# ℹ 8 more variables: survey <chr>, admin1_code <chr>, admin2_code <chr>,
# admin3_code <chr>, admin1_desc <chr>, admin2_desc <chr>, admin3_desc <chr>,
# year <chr>
$Tanzania21
# A tibble: 4,709 × 11
survey_year hhid country interview_date survey region_name region_code
<int> <chr> <chr> <date> <chr> <chr> <dbl>
1 2021 1000-001-01 Tanzan… 2021-09-15 Tanza… arusha 2
2 2021 1000-001-02 Tanzan… 2021-09-15 Tanza… arusha 2
3 2021 1000-001-03 Tanzan… 2021-09-15 Tanza… arusha 2
4 2021 1000-001-06 Tanzan… 2021-09-15 Tanza… arusha 2
5 2021 1001-001-01 Tanzan… 2021-09-15 Tanza… arusha 2
6 2021 1002-001-01 Tanzan… 2021-09-15 Tanza… arusha 2
7 2021 1003-001-01 Tanzan… 2021-09-15 Tanza… arusha 2
8 2021 1005-001-01 Tanzan… 2021-09-15 Tanza… arusha 2
9 2021 1006-001-01 Tanzan… 2021-09-15 Tanza… arusha 2
10 2021 1006-001-03 Tanzan… 2021-09-15 Tanza… arusha 2
# ℹ 4,699 more rows
# ℹ 4 more variables: district_name <chr>, district_code <dbl>,
# ward_code <dbl>, village_code <dbl>ys_coord_srvy <- setdiff(surveys, no_coord_srvy)
srvy_coord_cntry <- srvy |>
dplyr::filter(survey != "Nigeria18") |>
dplyr::filter(!is.na(lat) & lat > -999999999) |>
dplyr::filter(!(lat == 0.00000 & lon == 0.00000)) |>
dplyr::select(c(survey, country, hhid, lat, lon, interview_date)) |>
dplyr::group_by(survey) |>
dplyr::group_split() |>
setNames(ys_coord_srvy)
srvy_coord_cntry<list_of<
tbl_df<
survey : character
country : character
hhid : character
lat : double
lon : double
interview_date: date
>
>[5]>
$BurkinaFaso22
# A tibble: 3,227 × 6
survey country hhid lat lon interview_date
<chr> <chr> <chr> <dbl> <dbl> <date>
1 BurkinaFaso22 Burkina Faso 002001 14.4 -0.231 2021-12-21
2 BurkinaFaso22 Burkina Faso 002002 14.4 -0.231 2021-12-21
3 BurkinaFaso22 Burkina Faso 002004 14.4 -0.231 2021-12-21
4 BurkinaFaso22 Burkina Faso 002005 14.4 -0.231 2021-12-21
5 BurkinaFaso22 Burkina Faso 002007 14.4 -0.231 2021-12-21
6 BurkinaFaso22 Burkina Faso 002040 14.5 -0.234 2021-12-22
7 BurkinaFaso22 Burkina Faso 003008 14.0 -0.0260 2022-06-29
8 BurkinaFaso22 Burkina Faso 003019 12.4 -1.54 2022-07-25
9 BurkinaFaso22 Burkina Faso 003021 14.0 -0.0320 2022-06-26
10 BurkinaFaso22 Burkina Faso 003027 14.0 -0.0260 2022-06-22
# ℹ 3,217 more rows
$Ethiopia19
# A tibble: 17,830 × 6
survey country hhid lat lon interview_date
<chr> <chr> <chr> <dbl> <dbl> <date>
1 Ethiopia19 Ethiopia 010101088800910007 14.3 37.8 2019-06-19
2 Ethiopia19 Ethiopia 010101088800910017 14.3 37.8 2019-06-18
3 Ethiopia19 Ethiopia 010101088800910026 14.3 37.8 2019-06-18
4 Ethiopia19 Ethiopia 010101088800910029 14.3 37.8 2019-06-17
5 Ethiopia19 Ethiopia 010101088800910038 14.3 37.8 2019-06-07
6 Ethiopia19 Ethiopia 010101088800910046 14.3 37.8 2019-06-04
7 Ethiopia19 Ethiopia 010101088800910054 14.3 37.8 2019-06-05
8 Ethiopia19 Ethiopia 010101088800910062 14.3 37.8 2019-06-08
9 Ethiopia19 Ethiopia 010101088800910070 14.3 37.8 2019-06-17
10 Ethiopia19 Ethiopia 010101088800910082 14.3 37.8 2019-06-06
# ℹ 17,820 more rows
$Malawi19
# A tibble: 16,955 × 6
survey country hhid lat lon interview_date
<chr> <chr> <chr> <dbl> <dbl> <date>
1 Malawi19 Malawi 101011000014 -9.70 33.2 2019-08-29
2 Malawi19 Malawi 101011000023 -9.70 33.2 2019-08-29
3 Malawi19 Malawi 101011000040 -9.70 33.2 2019-08-28
4 Malawi19 Malawi 101011000071 -9.70 33.2 2019-08-29
5 Malawi19 Malawi 101011000095 -9.70 33.2 2019-08-28
6 Malawi19 Malawi 101011000115 -9.70 33.2 2019-08-29
7 Malawi19 Malawi 101011000126 -9.70 33.2 2019-08-28
8 Malawi19 Malawi 101011000135 -9.70 33.2 2019-08-29
9 Malawi19 Malawi 101011000183 -9.70 33.2 2019-08-28
10 Malawi19 Malawi 101011000190 -9.70 33.2 2019-08-28
# ℹ 16,945 more rows
$Mali22
# A tibble: 6,119 × 6
survey country hhid lat lon interview_date
<chr> <chr> <chr> <dbl> <dbl> <date>
1 Mali22 Mali 00101 14.7 -12.2 2021-11-10
2 Mali22 Mali 00102 14.7 -12.2 2021-11-10
3 Mali22 Mali 00103 14.7 -12.2 2021-11-11
4 Mali22 Mali 00104 14.7 -12.2 2021-11-11
5 Mali22 Mali 00105 14.7 -12.2 2021-11-10
6 Mali22 Mali 00106 14.7 -12.2 2021-11-10
7 Mali22 Mali 00107 14.7 -12.2 2021-11-10
8 Mali22 Mali 00108 14.7 -12.2 2021-11-10
9 Mali22 Mali 00109 14.7 -12.2 2021-11-10
10 Mali22 Mali 00110 14.7 -12.2 2021-11-12
# ℹ 6,109 more rows
$Togo22
# A tibble: 6,462 × 6
survey country hhid lat lon interview_date
<chr> <chr> <chr> <dbl> <dbl> <date>
1 Togo22 Togo 001002 6.16 1.23 2021-11-13
2 Togo22 Togo 001004 6.16 1.23 2021-11-14
3 Togo22 Togo 001005 6.16 1.23 2021-11-15
4 Togo22 Togo 001006 6.16 1.23 2021-11-15
5 Togo22 Togo 001007 6.16 1.23 2021-11-14
6 Togo22 Togo 001008 6.16 1.23 2021-11-13
7 Togo22 Togo 001009 6.16 1.23 2021-11-14
8 Togo22 Togo 001010 6.16 1.23 2021-11-15
9 Togo22 Togo 001011 6.16 1.23 2021-11-15
10 Togo22 Togo 001013 6.16 1.23 2021-11-15
# ℹ 6,452 more rows2.2.2 Weather data
To weather data is stored as nc files. We can read them with the function terra::rast().
Note how all the data sets have the same coordinate reference system (CRS), i.e. EPSG:4326. This is important because in this way all the data can “spatially” talk to each other.
tpre <- terra::rast(path_to_pre_monthly)
names(tpre) <- terra::names(tpre) |> second_to_date()
tevo <- terra::rast(path_to_evo_monthly)
names(tevo) <- terra::names(tevo) |> second_to_date()2.2.3 Administrative Boundaries
We now move to read the administrative divisions. We use the function read_adm_div() to do so. This function looks for spatial polygons for the iso and lvl provided provided.
Even if we have the coordinates from the survey, we will extract some spatial variables at the administrative division.
adm_div <- read_geoBoundaries(path_to_adm_div,
iso = c("BFA", "ETH", "MLI", "MWI", "TZA", "TGO"),
lvl = 2) |>
setNames(c("BurkinaFaso22", "Ethiopia19", "Mali22", "Malawi19",
"Togo22", "Tanzania21"))
Nigeria <- read_geoBoundaries(path_to_adm_div,
iso = c("NGA"),
lvl = 1)
adm_div$Nigeria18 <- Nigeria
adm_div <- adm_div[sort(names(adm_div))]
adm_div$BurkinaFaso22 class : SpatVector
geometry : polygons
dimensions : 45, 4 (geometries, attributes)
extent : -5.51892, 2.4054, 9.40111, 15.08259 (xmin, xmax, ymin, ymax)
coord. ref. : lon/lat WGS 84 (EPSG:4326)
names : ID_adm_div iso adm_div_1 adm_div_2
type : <chr> <chr> <chr> <chr>
values : 1 BFA Boucle du Mouhoun Bale
2 BFA Boucle du Mouhoun Mouhoun
3 BFA Boucle du Mouhoun Nayala2.3 Georeference the Surveys
srvy_coord_geo <- srvy_coord_cntry |>
list(purrr::keep_at(adm_div, ys_coord_srvy), names(srvy_coord_cntry)) |>
purrr::pmap(get_poly_attr_for_point)srvy_adm_geo <- srvy_adm_cntry
srvy_adm_geo$Nigeria18 <- srvy_adm_cntry$Nigeria18 |>
dplyr::mutate(adm_div_1 = stringr::str_to_title(admin1),
adm_div_1 = dplyr::case_when(
adm_div_1 == "Fct" ~ "Abuja Federal Capital Territory",
.default = adm_div_1))
srvy_adm_geo$Tanzania21 <- srvy_adm_cntry$Tanzania21 |>
dplyr::mutate(adm_div_1 = stringr::str_to_title(region_name),
adm_div_2 = stringr::str_to_title(district_name),
adm_div_1 = dplyr::case_when(
adm_div_1 == "Dar Es Salaam" ~ "Dar es Salaam",
adm_div_1 == "Kaskazini Pemba" ~ "North Pemba",
adm_div_1 == "Kaskazini Unguja" ~ "Zanzibar North",
adm_div_1 == "Kusini Pemba" ~ "South Pemba",
adm_div_1 == "Kusini Unguja" ~ "Zanzibar South & Central",
adm_div_1 == "Mjini Magharibi Unguja" ~ "Zanzibar Urban/West",
# Songwe was created the 2016 from the western half of Mbeya Region
adm_div_1 == "Songwe" ~ "Mbeya",
# wrong adm_div based on district
adm_div_2 == "Masasi Rural" ~ "Mtwara",
adm_div_2 == "Kigamboni" ~ "Dar es Salaam",
grepl("Kahama", adm_div_2) & adm_div_1 == "Katavi" ~ "Shinyanga", # !!!
adm_div_2 == "Chakechake" ~ "South Pemba",
adm_div_2 == "Kinondoni" ~ "Dar es Salaam",
adm_div_2 == "Songea" ~ "Ruvuma",
grepl("Songea", adm_div_2) ~ "Ruvuma",
adm_div_2 == "Ngorongoro" ~ "Arusha",
adm_div_2 == "Wete" ~ "North Pemba",
adm_div_2 == "Tandahimba" ~ "Mtwara",
adm_div_2 == "Babati" ~ "Manyara",
grepl("Baba", adm_div_2) ~ "Manyara", # !!!!
grepl("Nzega", adm_div_2) ~ "Tabora",
adm_div_2 == "Makete" ~ "Njombe",
adm_div_2 == "Mbeya Urban" ~ "Mbeya",
.default = adm_div_1),
adm_div_2 = dplyr::case_when(
# adm_div_1 Arusha
adm_div_2 == "Arusha Rural" ~ "Arusha",
# adm_div_1 Shinyanga
adm_div_2 == "Kahama Rural" ~ "Kahama",
adm_div_2 == "Kahama Town" ~ "Kahama Township Authority",
adm_div_2 == "Shinyanga Rural" ~ "Shinyanga",
adm_div_2 == "Nzega Town" ~ "Nzega",
# in 2012 by splitting the Kahama District into Msalala and Ushetu
adm_div_2 == "Msalala" ~ "Kahama",
adm_div_2 == "Ushetu" ~ "Kahama",
# adm_div_1 Katavi
adm_div_2 == "Mpanda Rural" ~ "Mpanda",
# established in 2012 from Mlele
adm_div_2 == "Mpimbwe" ~ "Mlele",
adm_div_2 == "Nsimbo" ~ "Mlele",
# adm_div_1 Singida
adm_div_2 == "Singida Rural" ~ "Singida",
# established in 2015 from Manyoni
adm_div_2 == "Itigi" ~ "Manyoni",
# adm_div_1 Dar es Salaam
# In 2015 Temeke was divided into Temeke and Kigamboni
adm_div_2 == "Kigamboni" ~ "Temeke",
# Kinondoni should be onlyu the eastern part
adm_div_2 == "Ubungo" ~ "Kinondoni",
# adm_div_1 Kagera
adm_div_2 == "Bukoba Rural" ~ "Bukoba",
# adm_div_1 Dodoma
adm_div_2 == "Kondoa Urban" ~ "Kondoa",
# adm_div_1 Mbeya
adm_div_2 == "Mbeya Rural" ~ "Mbeya",
adm_div_2 == "Mbalali" ~ "Mbarali",
# created in 2013, not perfect geo match
adm_div_2 == "Busokelo" ~ "Makete",
# adm_div_1 Morogoro
adm_div_2 == "Ifakara Urban" ~ "Kilombero",
adm_div_2 == "Morogoro Rural" ~ "Morogoro",
# it should be the western part of Ulanga
adm_div_2 == "Malinyi" ~ "Ulanga",
# adm_div_1 Kigoma
adm_div_2 == "Kasulu Rural" ~ "Kasulu",
adm_div_2 == "Kasulu Town" ~ "Kasulu Township Authority",
adm_div_2 == "Kigoma Rural" ~ "Kigoma",
adm_div_2 == "Kigoma Ujiji Urban" ~ "Kigoma Urban",
# adm_div_1 Mtwara
adm_div_2 == "Masasi Rural" ~ "Masasi",
adm_div_2 == "Masasi Urban" ~ "Masasi Township Authority",
adm_div_2 == "Mtwara Mikindani" ~ "Mtwara Urban",
adm_div_2 == "Mtwara Rural" ~ "Mtwara",
# adm_div_1 Geita
adm_div_2 == "Geita Town" ~ "Geita",
# adm_div_1 Mwanza
adm_div_2 == "Mwanza Urban" ~ "Nyamagana",
# created in 2015, from the eastern part of Sengerema
adm_div_2 == "Buchosa" ~ "Sengerema",
# adm_div_1 Iringa
adm_div_2 == "Iringa Rural" ~ "Iringa",
adm_div_2 == "Mafinga Town" ~ "Mafinga Township Authority",
# adm_div_1 Njombe
adm_div_2 == "Makambako Town" ~ "Makambako Township Authority",
adm_div_2 == "Mbeya Urban" ~ "Mbeya",
adm_div_2 == "Njombe Rural" ~ "Njombe",
adm_div_2 == "Njombe Town" ~ "Njombe Urban",
# adm_div_1 South Pemba
adm_div_2 == "Chakechake" ~ "Chake Chake",
# adm_div_1 Lindi
adm_div_2 == "Lindi Rural" ~ "Lindi",
# adm_div_1 Manyara
adm_div_2 == "Babati Rural" ~ "Babati",
adm_div_2 == "Babati Town" ~ "Babati UrbanBabati Urban",
adm_div_2 == "Mbulu Town" ~ "Mbulu",
# adm_div_1 Mara
adm_div_2 == "Butiama" ~ "Babati",
adm_div_2 == "Musoma Rural" ~ "Musoma",
# adm_div_1 Ruvuma
adm_div_2 == "Songea Rural" ~ "Songea",
# not clear
adm_div_2 == "Madaba" ~ "Songea",
# adm_div_1 Simiyu
adm_div_2 == "Bariadi Town" ~ "Bariadi",
# adm_div_1 Rukwa
adm_div_2 == "Sumbawanga Rural" ~ "Sumbawanga",
# adm_div_1 Pwani
adm_div_2 == "Kibaha Rural" ~ "Kibaha",
# Should be the eastern part of Rufiji
adm_div_2 == "Kibiti" ~ "Rufiji",
# created in 2016 from northen part of Bagamoyo
adm_div_2 == "Chalinze" ~ "Bagamoyo",
# adm_div_1 Tanga
adm_div_2 == "Korogwe Rural" ~ "Korogwe",
# Created in 2013 from Lushoto
adm_div_2 == "Bumbuli" ~ "Lushoto",
# adm_div_1 Kilimanjaro
adm_div_2 == "Moshi Rural" ~ "Moshi",
.default = adm_div_2))2.4 Crop the spatial variables
The spatial variables variables we have just load have a global coverage. It might be convenient to reduce the coverage to just the countries we are interested in. We can do this by using the terra::crop() function and the administrative divisions.
tpre_cntry <- purrr::map(adm_div,
crop_with_buffer,
raster = tpre,
buffer = 1)
tevo_cntry <- purrr::map(adm_div,
crop_with_buffer,
raster = tevo,
buffer = 1)2.5 Weather Variable Transformation
# From meter to millimeters
pre_cntry_mm <- purrr::map(tpre_cntry, ~ .x*1000)
tevo_cntry_mm <- purrr::map(tevo_cntry, ~ .x*1000)
wb_cntry_mm <- purrr::map2(pre_cntry_mm, tevo_cntry_mm, \(x, y) x + y)2.6 Extract
For the extraction of the weather variables, we use the function extract_cell_by_poly(). This function doesn’t aggregate the values within the polygons but extract all the cell values within the polygon separately. This is useful for us as we want to compute the SPI and SPEI for each cell and only later aggregate at the polygon level.
tpre_adm <- purrr::map2(pre_cntry_mm,
adm_div,
extract_cell_by_poly)
wb_adm <- purrr::map2(wb_cntry_mm,
adm_div,
extract_cell_by_poly)2.7 Compute SPI and SPEI
We now compute the SPI and the SPEI with the function compute_spei() and compute_spi(). These functions requires the water balance and the precipitation time series for each location and the time scale at which the indices are computed.
To compute the SPEI, it is recommended to use at least 30 years of observation to ensure a good estimation of the parameters. More years can strength the estimation but the results can be affected by climate change: if there have been a change in the climate parameters, old observations might be not indicative of the current situation affecting the estimation. There are no clear rule on this, so we leave add the possibility to select the time range of observation with the function select_by_dates(). The function requires both or just one between the starting date, from, and the end date to. If both are provide the the function select between the two dates, if only from is provided the function selects all date after, and if only to is provided the function selects all date before.
Looking at the result, we see first is the ID column, that we will use to merge back with the survey. The other columns contain the SPEI observations over time specific to each coordinate.
spei6 <- purrr::map2(wb_adm,
names(wb_adm),
compute_spei,
time_scale = 6) |>
purrr::map(agg_to_adm_div,
match_col = "^X[0-9]")Computing SPEI: BurkinaFaso22
Computing SPEI: Ethiopia19
Computing SPEI: Malawi19
Computing SPEI: Mali22
Computing SPEI: Nigeria18
Computing SPEI: Tanzania21
Computing SPEI: Togo22 spei3 <- purrr::map2(wb_adm,
names(wb_adm),
compute_spei,
time_scale = 3) |>
purrr::map(agg_to_adm_div,
match_col = "^X[0-9]")Computing SPEI: BurkinaFaso22
Computing SPEI: Ethiopia19
Computing SPEI: Malawi19
Computing SPEI: Mali22
Computing SPEI: Nigeria18
Computing SPEI: Tanzania21
Computing SPEI: Togo22 spei1 <- purrr::map2(wb_adm,
names(wb_adm),
compute_spei,
time_scale = 1) |>
purrr::map(agg_to_adm_div,
match_col = "^X[0-9]")Computing SPEI: BurkinaFaso22
Computing SPEI: Ethiopia19
Computing SPEI: Malawi19
Computing SPEI: Mali22
Computing SPEI: Nigeria18
Computing SPEI: Tanzania21
Computing SPEI: Togo22 spi6 <- purrr::map2(tpre_adm,
names(tpre_adm),
compute_spi,
time_scale = 6) |>
purrr::map(agg_to_adm_div,
match_col = "^X[0-9]")Computing SPI: BurkinaFaso22
Computing SPI: Ethiopia19
Computing SPI: Malawi19
Computing SPI: Mali22
Computing SPI: Nigeria18
Computing SPI: Tanzania21
Computing SPI: Togo22 spi3 <- purrr::map2(tpre_adm,
names(tpre_adm),
compute_spi,
time_scale = 3) |>
purrr::map(agg_to_adm_div,
match_col = "^X[0-9]")Computing SPI: BurkinaFaso22
Computing SPI: Ethiopia19
Computing SPI: Malawi19
Computing SPI: Mali22
Computing SPI: Nigeria18
Computing SPI: Tanzania21
Computing SPI: Togo22 spi1 <- purrr::map2(tpre_adm,
names(tpre_adm),
compute_spi,
time_scale = 1) |>
purrr::map(agg_to_adm_div,
match_col = "^X[0-9]")Computing SPI: BurkinaFaso22
Computing SPI: Ethiopia19
Computing SPI: Malawi19
Computing SPI: Mali22
Computing SPI: Nigeria18
Computing SPI: Tanzania21
Computing SPI: Togo22 We have computed the standardized indicators for each cells but we need additional information to assign each cells to the administrative divisions. We take advantage of the column ID_adm_div to merge the standardized indices with the administrative divisions.
spei1_adm <- purrr::map(adm_div, terra::values) |>
purrr::map2(spei1, merge_by_common)
spei3_adm <- purrr::map(adm_div, terra::values) |>
purrr::map2(spei3, merge_by_common)
spei6_adm <- purrr::map(adm_div, terra::values) |>
purrr::map2(spei6, merge_by_common)spi1_adm <- purrr::map(adm_div, terra::values) |>
purrr::map2(spi1, merge_by_common)
spi3_adm <- purrr::map(adm_div, terra::values) |>
purrr::map2(spi3, merge_by_common)
spi6_adm <- purrr::map(adm_div, terra::values) |>
purrr::map2(spi6, merge_by_common)2.8 Merge with Survey
srvy_all_geo <- c(srvy_adm_geo, srvy_coord_geo)
srvy_all_geo <- srvy_all_geo[sort(names(srvy_all_geo))]
spi1_hh <- purrr::map2(srvy_all_geo,
spi1_adm,
merge_by_common) |>
purrr::map(select_by_interview,
interview = interview_date,
interval = "2 year",
wide = FALSE)Missing interview are dropped!
Missing interview are dropped!
Missing interview are dropped!
Missing interview are dropped!spi3_hh <- purrr::map2(srvy_all_geo,
spi3_adm,
merge_by_common) |>
purrr::map(select_by_interview,
interview = interview_date,
interval = "2 year",
wide = FALSE)Missing interview are dropped!
Missing interview are dropped!
Missing interview are dropped!
Missing interview are dropped!spi6_hh <- purrr::map2(srvy_all_geo,
spi6_adm,
merge_by_common) |>
purrr::map(select_by_interview,
interview = interview_date,
interval = "2 year",
wide = FALSE)Missing interview are dropped!
Missing interview are dropped!
Missing interview are dropped!
Missing interview are dropped!spei1_hh <- purrr::map2(srvy_all_geo,
spei1_adm,
merge_by_common) |>
purrr::map(select_by_interview,
interview = interview_date,
interval = "2 year",
wide = FALSE)Missing interview are dropped!
Missing interview are dropped!
Missing interview are dropped!
Missing interview are dropped!spei3_hh <- purrr::map2(srvy_all_geo,
spei3_adm,
merge_by_common) |>
purrr::map(select_by_interview,
interview = interview_date,
interval = "2 year",
wide = FALSE)Missing interview are dropped!
Missing interview are dropped!
Missing interview are dropped!
Missing interview are dropped!spei6_hh <- purrr::map2(srvy_all_geo,
spei6_adm,
merge_by_common) |>
purrr::map(select_by_interview,
interview = interview_date,
interval = "2 year",
wide = FALSE)Missing interview are dropped!
Missing interview are dropped!
Missing interview are dropped!
Missing interview are dropped!2.9 Save
The final step of the code is to save the result. In this case, we save it as a dta file using the haven::write_dta() function.
spei6_hh |>
dplyr::bind_rows() |>
haven::write_dta(file.path(path_to_result, "spei6.dta"))
spei3_hh |>
dplyr::bind_rows() |>
haven::write_dta(file.path(path_to_result, "spei3.dta"))
spei1_hh |>
dplyr::bind_rows() |>
haven::write_dta(file.path(path_to_result, "spei1.dta"))
spi6_hh |>
dplyr::bind_rows() |>
haven::write_dta(file.path(path_to_result, "spi6.dta"))
spi3_hh |>
dplyr::bind_rows() |>
haven::write_dta(file.path(path_to_result, "spi3.dta"))
spi1_hh |>
dplyr::bind_rows() |>
haven::write_dta(file.path(path_to_result, "spi1.dta"))